A Neural Based Allocation Architecture Of Mobile Computing
[Full Text]
AUTHOR(S)
Sudhir Kumar Sharma, Dr. Wiqas Ghai
KEYWORDS
Mobile Cloud, Applications, Architectures, Scheduling, and Load Management, MBFD, ANN.
ABSTRACT
The computing architectures these days are getting sophisticated. The common person is aiming to get the services on the go. Researchers, therefore, envisage expanding cloud computing facilities to smart-phones limitations. The difficulty is that conventional smart-phone implementation designs do not promote the creation of apps that can integrate cloud-computing characteristics and require specific mobile cloud application designs. Scheduling, security, and load management is an essential part of the Cloud computing application architecture as well as mobile computing architecture. This paper presents an energy efficiency mobile computing architecture by using the Modified Best Fit Decreasing (MBFD) algorithm with Artificial neural Network (ANN) as a machine learning approach. The tasks are sorted using MBFD approach and the problem (Over-loading and under-loading) the server is resolved using ANN as a classification approach. The results shows that the tasks completed by mobile servers using ANN approach with less time and also required minimum energy to complete.
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